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<html lang="zh">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
    <meta http-equiv="X-UA-Compatible" content="ie=edge">
    <title>canvas画布的高斯模糊效果</title>
</head>
<body>
<canvas id="canvas"></canvas>
</body>
<script>
    var canvas = document.getElementById('canvas');
    var ctx = canvas.getContext('2d');
    let img = new Image();
    //这里直接修改图片的路径
    img.src = "https://segmentfault.com/img/bVbFc22";
    // 解决跨域问题
    img.crossOrigin = "Anonymous";
    img.onload = function () {
        //设置canvas的宽高
        canvas.height = img.height;
        canvas.width = img.width;
        //将图像绘制到canvas上面
        ctx.drawImage(img, 0, 0, img.width, img.height);
        //从画布获取一半图像
        var data = ctx.getImageData(0, 0, img.width/2, img.height);
        //将图像数据进行高斯模糊 data.data是一个数组，每四个值代表一个像素点的rgba的值，data.width data.height 分别代表图像数据的宽高
        var emptyData = gaussBlur(data);
        //将模糊的图像数据再渲染到画布上面
        ctx.putImageData(emptyData, 0, 0);
    };

    function gaussBlur(imgData) {
        var pixes = imgData.data;
        var width = imgData.width;
        var height = imgData.height;
        var gaussMatrix = [],
            gaussSum = 0,
            x, y,
            r, g, b, a,
            i, j, k, len;

        var radius = 10;
        var sigma = 5;

        a = 1 / (Math.sqrt(2 * Math.PI) * sigma);
        b = -1 / (2 * sigma * sigma);
        //生成高斯矩阵
        for (i = 0, x = -radius; x <= radius; x++, i++) {
            g = a * Math.exp(b * x * x);
            gaussMatrix[i] = g;
            gaussSum += g;

        }

        //归一化, 保证高斯矩阵的值在[0,1]之间
        for (i = 0, len = gaussMatrix.length; i < len; i++) {
            gaussMatrix[i] /= gaussSum;
        }
        //x 方向一维高斯运算
        for (y = 0; y < height; y++) {
            for (x = 0; x < width; x++) {
                r = g = b = a = 0;
                gaussSum = 0;
                for (j = -radius; j <= radius; j++) {
                    k = x + j;
                    if (k >= 0 && k < width) {//确保 k 没超出 x 的范围
                        //r,g,b,a 四个一组
                        i = (y * width + k) * 4;
                        r += pixes[i] * gaussMatrix[j + radius];
                        g += pixes[i + 1] * gaussMatrix[j + radius];
                        b += pixes[i + 2] * gaussMatrix[j + radius];
                        // a += pixes[i + 3] * gaussMatrix[j];
                        gaussSum += gaussMatrix[j + radius];
                    }
                }
                i = (y * width + x) * 4;
                // 除以 gaussSum 是为了消除处于边缘的像素, 高斯运算不足的问题
                // console.log(gaussSum)
                pixes[i] = r / gaussSum;
                pixes[i + 1] = g / gaussSum;
                pixes[i + 2] = b / gaussSum;
                // pixes[i + 3] = a ;
            }
        }
        //y 方向一维高斯运算
        for (x = 0; x < width; x++) {
            for (y = 0; y < height; y++) {
                r = g = b = a = 0;
                gaussSum = 0;
                for (j = -radius; j <= radius; j++) {
                    k = y + j;
                    if (k >= 0 && k < height) {//确保 k 没超出 y 的范围
                        i = (k * width + x) * 4;
                        r += pixes[i] * gaussMatrix[j + radius];
                        g += pixes[i + 1] * gaussMatrix[j + radius];
                        b += pixes[i + 2] * gaussMatrix[j + radius];
                        // a += pixes[i + 3] * gaussMatrix[j];
                        gaussSum += gaussMatrix[j + radius];
                    }
                }
                i = (y * width + x) * 4;
                pixes[i] = r / gaussSum;
                pixes[i + 1] = g / gaussSum;
                pixes[i + 2] = b / gaussSum;
            }
        }
        return imgData;
    }
</script>
</html>
